def compute_module_count(inf, is_mmod):
if is_mmod:
mods = eval_util.read_mmods(inf)
else:
mods = eval_util.read_rmods(inf)
return len(mods)
def compute_efficacy(inf, is_rmod, is_partial_module, S, mets, reacts):
if is_rmod:
mods = eval_util.read_mmods(inf)
else:
mods = eval_util.read_rmods(inf)
sizes = []
for mod_name, mod_metabs in mods.iteritems():
sizes.append(len(mod_metabs))
if is_partial_module:
pass
# TODO:PARTIAL: should create one big module of unused ones!? NO! DO NOTHING JUST IGNORE THEM!
N = sum(sizes)
p = efficacy_empirical_p(N)
if len(sizes) != 0:
efficacy = 1.0 * (math.log(efficacy_f(N, alpha, p)) - math.log(
efficacy_F(sizes, len(sizes), alpha, p))) * 1.0 / (
math.log(efficacy_f(N, alpha, p)) -
math.log(2 * efficacy_f(math.sqrt(N), alpha, p)))
else:
efficacy = 0.0
return efficacy
def general_compute_distance(cached_reaction_similarities, similarity_table, table_gene_order, inf, species, criteria, factor1, factor2, is_mmod, is_partial_module, S, mets, reacts, revs, go_loaded_model, moredata_loaded_model):
if is_mmod:
mmods = eval_util.read_mmods(inf)
mods = eval_util.convert_metabmod_to_reactmod(mmods, is_partial_module, S, mets, reacts)
else:
mods = eval_util.read_rmods(inf)
# TODO:PARTIAL: all reactions that are not counted in modules (NO: are assigned to external module
# and) -1 (like blocked reactions) is placed for couplings (in associated rows and columns)
if is_partial_module:
done_reacts = set()
for mod_n, mod_rs in mods.iteritems():
done_reacts.update(mod_rs)
not_done_reacts = set(reacts) - set(done_reacts)
if len(not_done_reacts) > 0:
mods[eval_constants.EXTERNAL_MODULE_ID] = not_done_reacts
if species == 'arabidopsis_irs1597':
mods = filter_out_reactions_by_type(mods, [(0, 'R_EX')])
else:
mods = filter_out_reactions_by_type(mods, [(0, 'R_EX_')])
mods_similarity_table = {}
for mod1_name, mod1 in mods.iteritems():
mods_similarity_table[mod1_name] = {}
for mod2_name, mod2 in mods.iteritems():
sim = 0
for r1 in mod1:
for r2 in mod2:
if mod1_name != mod2_name or r1 != r2: # designed to work for overlapping modules. only filters out a gene-similarity-with-itself for intra-module
sim += compute_similarity_of_gene_pair(cached_reaction_similarities, r1, r2, similarity_table, species, criteria, factor1, factor2, go_loaded_model, moredata_loaded_model)
mods_similarity_table[mod1_name][mod2_name] = sim
# print 'mod simetable done'
# TODO:BUG: for overlapping modules this fails because (len(rxns) - len(mods[mod_name])) is wrong
intra_score = 0
inter_score = 0
for mod_name, mods_sim in mods_similarity_table.iteritems():
intra_piece = mods_sim[mod_name] * 1.0 / pow(len(mods[mod_name]), 2)
inter_piece = sum([mods_sim[other_mod_name] * 1.0 / (len(mods[mod_name]) * len(mods[other_mod_name])) for other_mod_name in mods_similarity_table.iterkeys() if other_mod_name != mod_name])
if mod_name == eval_constants.EXTERNAL_MODULE_ID:
intra_score -= intra_piece
inter_score -= inter_piece
else:
intra_score += intra_piece
inter_score += inter_piece
# print 'done'
return intra_score - inter_score
def compute_cohesion_coupling(inf, is_mmod, is_partial_module, S, mets, reacts,
revs):
couplings, blocks = compute_couplings(
S, mets, reacts, revs,
max(map(lambda s: len(s), reacts)) + 1,
max(map(lambda s: len(s), mets)) + 1)
if is_mmod:
mmods = eval_util.read_mmods(inf)
mods = eval_util.convert_metabmod_to_reactmod(mmods, is_partial_module,
S, mets, reacts)
else:
mods = eval_util.read_rmods(inf)
# TODO:PARTIAL: all reactions that are not counted in modules (NO: are assigned to external module
# and) -1 (like blocked reactions) is placed for couplings (in associated rows and columns)
if is_partial_module:
done_reacts = set()
for mod_n, mod_rs in mods.iteritems():
done_reacts.update(mod_rs)
not_done_reacts = set(reacts) - set(done_reacts)
if len(not_done_reacts) > 0:
mods[eval_constants.EXTERNAL_MODULE_ID] = not_done_reacts
couplings = copy.deepcopy(couplings)
for r in not_done_reacts:
r_idx = reacts.index(r)
for i in range(len(reacts)):
couplings[i][r_idx] = -1
couplings[r_idx][i] = -1
module_coupling_table, module_order_in_header = compute_module_coupling_table(
couplings, blocks, mods, reacts)
return compute_coupling_uncoupling_score(module_coupling_table,
module_order_in_header)
def compute_and_print_size_distribution(inf, is_mmod, species, method):
out_dir = '%s/sizes' % my_constants.evalResultPath
my_util.mkdir_p(out_dir)
if is_mmod:
mods = eval_util.read_mmods(inf)
else:
mods = eval_util.read_rmods(inf)
sizes = []
for mod_name, mod_metabs in mods.iteritems():
sizes.append(len(mod_metabs))
result_file = '%s/%s_%s.png' % (out_dir, species, method)
alt_result_file = '%s/sc_%s_%s.png' % (out_dir, species, method)
f = open('size_distrib.r', 'w')
write_line(f, 'png("%s")' % result_file)
write_line(
f, "hist(c(%s), main='%d modules (%s)', xlab='', ylab='')" %
(','.join([str(s) for s in sizes]), len(mods), species + ' ' + method))
write_line(f, 'dev.off()')
write_line(f, 'png("%s")' % alt_result_file)
write_line(
f,
"stripchart(c(%s), main='%d modules (%s)', method='stack', offset=0.5, pch=1)"
% (','.join([str(s)
for s in sizes]), len(mods), species + ' ' + method))
write_line(f, 'dev.off()')
f.close()
res = os.system('%s size_distrib.r' % my_constants.rScriptPath)
while not os.path.isfile(result_file):
time.sleep(5)
return '=HYPERLINK("%s")' % result_file